A method to estimate phenological variation using data from large‐scale abundance monitoring programmes

Abstract

Capsule Large-scale abundance monitoring programmes can be used to estimate annual phenological shifts. Aims Phenology refers to the timing of any annually repeated biological event. The method developed here aims at measuring phenological variation in an indirect way by modelling seasonal abundance variations. Thus, it provides the opportunity to use a large number of datasets which have rarely been used in phenological studies. Phenological variations computed using this standardized method are comparable between species. Methods The data used for the development of this method originates from the French Breeding Bird Survey, a large-scale abundance monitoring programme launched in 2001. For each species, the phenological shift between two seasonal abundance trends is computed using maximum likelihood. Results Phenological shifts relative to the year 2005 (reference year) were estimated for 46 species over a 5-year period (2001-6). The standard deviations of the shifts do not differ significantly between species with different migratory status. Moreover, at the species level, the computed phenological shifts relate to the shifts of the mean date weighted by abundance. However, mean date, cannot be used in studies incorporating species with different migratory status (e.g. trans-Saharan migrant, sedentary) because of ambiguous changes for the same biological shift in timing. Conclusions The method described here is of particular value in determining how the phenology of common bird species changes in relation to climate. It offers the opportunity to increase the spatial scale of phenological studies and to include multi-species analyses. This method could be applied to any abundance or constant effort site programme to study the timing of any biological process for which a seasonal distribution is available. Phenology has become a key research field in the understanding of the effects of climate change on animal or plant populations (Walther et al. 2002, Parmesan & Yohe 2003, Crick 2004, Parmesan 2006). However, until now hypotheses concerning the way global change is currently altering the timing of seasonal events of a species mostly originate from sampling protocols dealing with only a few species surveyed intensively at few study sites (Sparks et al. 2005, Jonzen et al. 2006). Moreover, most datasets that have until now been analyzed under the phenological framework originate from designs displaying many temporal but very few spatial replicates. This is a result of the trade-off existing between temporal and spatial replicates as the former are too costly to carry out at a large spatial scale. Among birds, for example, although migration studies are numerous, they are usually carried out at specific migration monitoring stations (Sparks &

Advanced departure dates in long-distance migratory raptors

“…Indeed, there is a strong correlation between estimations of the phenological shifts between the two methods which range between r = 0.55, n = 28, P = 0.002 for the Merlin (Falco columbarius), and r = 0 0.98, n = 28, P \ 0.001 for the European Sparrowhawk (Accipiter nisus) with a mean Pearson's coefficient r = 0.90 ± 0.11 for the 14 species. Methodological details of the smoothing method are available in Moussus et al (2009). The temporal trend of mean passage dates was modelled using linear models and tested using analyses of variance (ANOVAs).…”

Featuring 10 phenological estimators using simulated data

“…The recent development of statistical smoothing techniques has also allowed the use of nonparametric methods (Knudsen et al. 2007; Moussus et al. 2009).…”

“…These include the estimation of percentile dates (Jonzen et al 2006) or mean dates (Miller-Rushing et al 2008b). The recent development of statistical smoothing techniques has also allowed the use of nonparametric methods (Knudsen et al 2007;Moussus et al 2009). Thus, curve fitting methods using smoothing functions, such as splines, have been developed to estimate phenological variation from standardized monitoring programmes.…”

“…In this study, we compare 10 methods of estimation of phenological shifts using simulated data. We focus on first appearance dates, first 10 and 25 individuals' appearance dates, mean dates, 10, 25, 50, 75, 90 percentile dates and a recently developed curve fitting method using spline functions and maximum likelihood (Moussus et al 2009). Using root mean-squared errors (RMSE), we score the accuracy of these methods in relation to various parameters of the simulated but realistic seasonal distributions.…”

Estimating unbiased phenological trends by adapting site‐occupancy models

“…However, methodological differences and biased estimates of phenological shifts could also be a main reason for differing results among studies. One of the few exceptions is Moussus et al (2009), who proposed a method that is based on daily count data. One of the few exceptions is Moussus et al (2009), who proposed a method that is based on daily count data.…”